Item Response Warehouse

Metadata

Examine sample sizes and other dataset summary statistics.

vars = new Map([["Number responses", "n_responses"],
                ["Number participants", "n_participants"],
                ["Number items", "n_items"],
                ["Responses per participant", "responses_per_participant"],
                ["Responses per item", "responses_per_item"],
                ["Sparsity", "sparsity"]])

// vars = new Map(Object.entries(cont_vars))
// console.log(Array.from(vars.values())[1])
viewof x_var = Inputs.select(vars, {value: "n_items",        label: "X axis"})
viewof y_var = Inputs.select(vars, {value: "n_participants", label: "Y axis"})

// selector for color variable
default_color = "grey"
color_opts = new Map([["None", null],
                      ["Dichotomous vs. Polytomous", "partition"]])

viewof color_var = Inputs.select(color_opts, {label: "Color"})

plt_color = color_var || default_color

// histogram
viewof metadata_hist = Plot.plot({
      // x: {type: "log"},
      y: {grid: true},
  color: {legend: color_var != null},
  marks: [
    Plot.rectY(metadata_trans, Plot.binX({y: "count"}, {x: x_var, fill: plt_color})),
    Plot.ruleY([0])
  ]
})

// scatter plot
viewof metadata_scatter = Plot.plot({
      x: {type: "log"},
      y: {type: "log"},
   grid: true,
  color: {legend: color_var != null},
  marks: [
    // points
    Plot.dot(metadata_trans, {x: x_var, y: y_var, stroke: plt_color}),
    // tooltips
    Plot.tip(metadata_trans, Plot.pointer({x: x_var, y: y_var, stroke: plt_color, title: "dataset_name"})),
    // regression line
    Plot.linearRegressionY(metadata_trans, {x: x_var, y: y_var, stroke: plt_color})
  ]
})

// display histogram and scatter plots side by side
html`<div style="display: flex;">
 <div style="flex-basis:50%"> ${viewof metadata_hist}    </div>
 <div style="flex-basis:50%"> ${viewof metadata_scatter} </div>
</div>`

Individual datasets

Explore each dataset’s distribution of mean responses by item and by subject.

summaries_map = new Map(Object.entries(summaries))
viewof dataset = Inputs.select(summaries_map, {label: 'Dataset'})

// transpose all data for plotting
items = transpose(dataset.items)
subjects = transpose(dataset.subjects)
metadata_trans = transpose(metadata)

Plot = import("https://esm.sh/@observablehq/plot")

// plot items
viewof item_plot = Plot.plot({
      x: {label: "Item mean response"},
      y: {grid: true},
  marks: [
    Plot.rectY(items, Plot.binX({y: "count"}, {x: "mean"})),
    Plot.ruleY([0])
  ]
})

// plot subjects
viewof subj_plot = Plot.plot({
      x: {label: "Subject mean response"},
      y: {grid: true},
  marks: [
    Plot.rectY(subjects, Plot.binX({y: "count"}, {x: "mean"})),
    Plot.ruleY([0])
  ]
})

// display item and subject plots side by side
html`<div style="display: flex;">
 <div style="flex-basis:50%"> ${viewof item_plot} </div>
 <div style="flex-basis:50%"> ${viewof subj_plot} </div>
</div>`